OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH

Martin Zıaratı; Osman Nuri Uçan; Reza Zıaratı

TR EN

OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH

Abstract

In this paper, a Back Propagation-Artificial Neural Network BP-ANN has been adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. The conventional approach to determine the parts requirements is the Economic Order Quantity EOQ method. The ability of neural models to learn, particularly their capability of handling large amounts of data simultaneously as well as their fast response time, are the characteristics desired for predictive and forecasting purposes. Here, the actual data obtained from a major auto parts supplier chain, involving a multi-layer system of supplying auto parts to car dealers, have been used to optimise and develop a BP-ANN model. The model has shown promising results in predicting parts orders with high degree of accuracy.

Keywords

Artificial Neural Network (ANN), Economic Order Quantity (EOQ)

References

ZIARATI, M (2000), "Improving the Supply Chain in the Automotive Industry Using Kaizen Engineering" MPhil transfer report, De Montfort University, UK.
HAYKIN,S. (1999), " Neural Networks"John Wiley Pub.

Details

Primary Language

English

Subjects

-

Journal Section

-

Authors

Martin Zıaratı This is me

Osman Nuri Uçan This is me

Reza Zıaratı This is me

Publication Date

January 1, 2001

Submission Date

-

Acceptance Date

-

Published in Issue

Year 2001 Volume: 2 Number: 1

IZ

https://izlik.org/JA57TA88WT

Cite

RIS / Bibtex

APA

Zıaratı, M., Uçan, O. N., & Zıaratı, R. (2001). OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH. Doğuş Üniversitesi Dergisi, 2(1), 120-128. https://izlik.org/JA57TA88WT

AMA

1.Zıaratı M, Uçan ON, Zıaratı R. OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH. Doğuş Üniversitesi Dergisi. 2001;2(1):120-128. https://izlik.org/JA57TA88WT

Chicago

Zıaratı, Martin, Osman Nuri Uçan, and Reza Zıaratı. 2001. “OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH”. Doğuş Üniversitesi Dergisi 2 (1): 120-28. https://izlik.org/JA57TA88WT.

EndNote

Zıaratı M, Uçan ON, Zıaratı R (January 1, 2001) OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH. Doğuş Üniversitesi Dergisi 2 1 120–128.

IEEE

[1]M. Zıaratı, O. N. Uçan, and R. Zıaratı, “OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH”, Doğuş Üniversitesi Dergisi, vol. 2, no. 1, pp. 120–128, Jan. 2001, [Online]. Available: https://izlik.org/JA57TA88WT

ISNAD

Zıaratı, Martin - Uçan, Osman Nuri - Zıaratı, Reza. “OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH”. Doğuş Üniversitesi Dergisi 2/1 (January 1, 2001): 120-128. https://izlik.org/JA57TA88WT.

JAMA

1.Zıaratı M, Uçan ON, Zıaratı R. OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH. Doğuş Üniversitesi Dergisi. 2001;2:120–128.

MLA

Zıaratı, Martin, et al. “OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH”. Doğuş Üniversitesi Dergisi, vol. 2, no. 1, Jan. 2001, pp. 120-8, https://izlik.org/JA57TA88WT.

Vancouver

1.Martin Zıaratı, Osman Nuri Uçan, Reza Zıaratı. OPTIMISATION OF ECONOMIC ORDER QUANTITY USING NEURAL NETWORKS APPROACH. Doğuş Üniversitesi Dergisi [Internet]. 2001 Jan. 1;2(1):120-8. Available from: https://izlik.org/JA57TA88WT